OpenRaiser/Intern-Atlas huggingface dataset:
Measuring Impacts of Poisoning on Model Parameters and Embeddings for Large Language Models of Code
Venue: arXiv cs.SE 2024
Year: 2024

Large language models (LLMs) have revolutionized software development practices, yet concerns about their safety have arisen, particularly regarding hidden backdoors, aka trojans. Backdoor attacks involve the insertion of triggers into training data, allowing attackers to manipulate the behavior of the model maliciously. In this paper, we focus on analyzing the model parameters to detect potential backdoor signals in code models. Specifically, we examine attention weights and biases, and context embeddings of the clean and poisoned CodeBERT and CodeT5 models. Our results suggest noticeable patterns in context embeddings of poisoned samples for both the poisoned models; however, attention weights and biases do not show any significant differences. This work contributes to ongoing efforts in white-box detection of backdoor signals in LLMs of code through the analysis of parameters and embeddings.
CMA-ES with Adaptive Reevaluation for Multiplicative Noise
Venue: arXiv cs.NE 2024
Year: 2024

The covariance matrix adaptation evolution strategy (CMA-ES) is a powerful optimization method for continuous black-box optimization problems. Several noise-handling methods have been proposed to bring out the optimization performance of the CMA-ES on noisy objective functions. The adaptations of the population size and the learning rate are two major approaches that perform well under additive Gaussian noise. The reevaluation technique is another technique that evaluates each solution multiple times. In this paper, we discuss the difference between those methods from the perspective of stochastic relaxation that considers the maximization of the expected utility function. We derive that the set of maximizers of the noise-independent utility, which is used in the reevaluation technique, certainly contains the optimal solution, while the noise-dependent utility, which is used in the population size and leaning rate adaptations, does not satisfy it under multiplicative noise. Based on the discussion, we develop the reevaluation adaptation CMA-ES (RA-CMA-ES), which computes two update directions using half of the evaluations and adapts the number of reevaluations based on the estimated correlation of those two update directions. The numerical simulation shows that the RA-CMA-ES outperforms the comparative method under multiplicative noise, maintaining competitive performance under additive noise.
NubbleDrop: A Simple Way to Improve Matching Strategy for Prompted One-Shot Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

Driven by large data trained segmentation models, such as SAM , research in one-shot segmentation has experienced significant advancements. Recent contributions like PerSAM and MATCHER , presented at ICLR 2024, utilize a similar approach by leveraging SAM with one or a few reference images to generate high quality segmentation masks for target images. Specifically, they utilize raw encoded features to compute cosine similarity between patches within reference and target images along the channel dimension, effectively generating prompt points or boxes for the target images a technique referred to as the matching strategy. However, relying solely on raw features might introduce biases and lack robustness for such a complex task. To address this concern, we delve into the issues of feature interaction and uneven distribution inherent in raw feature based matching. In this paper, we propose a simple and training-free method to enhance the validity and robustness of the matching strategy at no additional computational cost (NubbleDrop). The core concept involves randomly dropping feature channels (setting them to zero) during the matching process, thereby preventing models from being influenced by channels containing deceptive information. This technique mimics discarding pathological nubbles, and it can be seamlessly applied to other similarity computing scenarios. We conduct a comprehensive set of experiments, considering a wide range of factors, to demonstrate the effectiveness and validity of our proposed method. Our results showcase the significant improvements achieved through this simmple and straightforward approach.
Unsupervised Image Prior via Prompt Learning and CLIP Semantic Guidance for Low-Light Image Enhancement
Venue: arXiv cs.CV 2024
Year: 2024

Currently, low-light conditions present a significant challenge for machine cognition. In this paper, rather than optimizing models by assuming that human and machine cognition are correlated, we use zero-reference low-light enhancement to improve the performance of downstream task models. We propose to improve the zero-reference low-light enhancement method by leveraging the rich visual-linguistic CLIP prior without any need for paired or unpaired normal-light data, which is laborious and difficult to collect. We propose a simple but effective strategy to learn prompts that help guide the enhancement method and experimentally show that the prompts learned without any need for normal-light data improve image contrast, reduce over-enhancement, and reduce noise over-amplification. Next, we propose to reuse the CLIP model for semantic guidance via zero-shot open vocabulary classification to optimize low-light enhancement for task-based performance rather than human visual perception. We conduct extensive experimental results showing that the proposed method leads to consistent improvements across various datasets regarding task-based performance and compare our method against state-of-the-art methods, showing favorable results across various low-light datasets.
Automated Coastline Extraction Using Edge Detection Algorithms
Venue: arXiv cs.CV 2024
Year: 2024

We analyse the effectiveness of edge detection algorithms for the purpose of automatically extracting coastlines from satellite images. Four algorithms - Canny, Sobel, Scharr and Prewitt are compared visually and using metrics. With an average SSIM of 0.8, Canny detected edges that were closest to the reference edges. However, the algorithm had difficulty distinguishing noisy edges, e.g. due to development, from coastline edges. In addition, histogram equalization and Gaussian blur were shown to improve the effectiveness of the edge detection algorithms by up to 1.5 and 1.6 times respectively.
The Effectiveness of Edge Detection Evaluation Metrics for Automated Coastline Detection
Venue: arXiv cs.CV 2024
Year: 2024

We analyse the effectiveness of RMSE, PSNR, SSIM and FOM for evaluating edge detection algorithms used for automated coastline detection. Typically, the accuracy of detected coastlines is assessed visually. This can be impractical on a large scale leading to the need for objective evaluation metrics. Hence, we conduct an experiment to find reliable metrics. We apply Canny edge detection to 95 coastline satellite images across 49 testing locations. We vary the Hysteresis thresholds and compare metric values to a visual analysis of detected edges. We found that FOM was the most reliable metric for selecting the best threshold. It could select a better threshold 92.6% of the time and the best threshold 66.3% of the time. This is compared RMSE, PSNR and SSIM which could select the best threshold 6.3%, 6.3% and 11.6% of the time respectively. We provide a reason for these results by reformulating RMSE, PSNR and SSIM in terms of confusion matrix measures. This suggests these metrics not only fail for this experiment but are not useful for evaluating edge detection in general.
MSNER: A Multilingual Speech Dataset for Named Entity Recognition
Venue: arXiv cs.CL 2024
Year: 2024

While extensively explored in text-based tasks, Named Entity Recognition (NER) remains largely neglected in spoken language understanding. Existing resources are limited to a single, English-only dataset. This paper addresses this gap by introducing MSNER, a freely available, multilingual speech corpus annotated with named entities. It provides annotations to the VoxPopuli dataset in four languages (Dutch, French, German, and Spanish). We have also releasing an efficient annotation tool that leverages automatic pre-annotations for faster manual refinement. This results in 590 and 15 hours of silver-annotated speech for training and validation, alongside a 17-hour, manually-annotated evaluation set. We further provide an analysis comparing silver and gold annotations. Finally, we present baseline NER models to stimulate further research on this newly available dataset.
On Performance of FAS-aided Wireless Powered NOMA Communication Systems
Venue: arXiv cs.IT 2024
Year: 2024

This paper studies the performance of a wireless powered communication network (WPCN) under the non-orthogonal multiple access (NOMA) scheme, where users take advantage of an emerging fluid antenna system (FAS). More precisely, we consider a scenario where a transmitter is powered by a remote power beacon (PB) to send information to the planar NOMA FAS-equipped users through Rayleigh fading channels. After introducing the distribution of the equivalent channel coefficients to the users, we derive compact analytical expressions for the outage probability (OP) in order to evaluate the system performance. Additionally, we present asymptotic OP in the high signal-to-noise ratio (SNR) regime. Eventually, results reveal that deploying the FAS with only one activated port in NOMA users can significantly enhance the WPCN performance compared with using traditional antenna systems (TAS).
Learning More Generalized Experts by Merging Experts in Mixture-of-Experts
Venue: arXiv cs.LG 2024
Year: 2024

We observe that incorporating a shared layer in a mixture-of-experts can lead to performance degradation. This leads us to hypothesize that learning shared features poses challenges in deep learning, potentially caused by the same feature being learned as various different features. To address this issue, we track each expert's usage frequency and merge the two most frequently selected experts. We then update the least frequently selected expert using the combination of experts. This approach, combined with the subsequent learning of the router's expert selection, allows the model to determine if the most frequently selected experts have learned the same feature differently. If they have, the combined expert can be further trained to learn a more general feature. Consequently, our algorithm enhances transfer learning and mitigates catastrophic forgetting when applied to multi-domain task incremental learning.
Knowledge Graph Pruning for Recommendation
Venue: arXiv cs.IR 2024
Year: 2024

Recent years have witnessed the prosperity of knowledge graph based recommendation system (KGRS), which enriches the representation of users, items, and entities by structural knowledge with striking improvement. Nevertheless, its unaffordable computational cost still limits researchers from exploring more sophisticated models. We observe that the bottleneck for training efficiency arises from the knowledge graph, which is plagued by the well-known issue of knowledge explosion. Recently, some works have attempted to slim the inflated KG via summarization techniques. However, these summarized nodes may ignore the collaborative signals and deviate from the facts that nodes in knowledge graph represent symbolic abstractions of entities from the real-world. To this end, in this paper, we propose a novel approach called KGTrimmer for knowledge graph pruning tailored for recommendation, to remove the unessential nodes while minimizing performance degradation. Specifically, we design an importance evaluator from a dual-view perspective. For the collective view, we embrace the idea of collective intelligence by extracting community consensus based on abundant collaborative signals, i.e. nodes are considered important if they attract attention of numerous users. For the holistic view, we learn a global mask to identify the valueless nodes from their inherent properties or overall popularity. Next, we build an end-to-end importance-aware graph neural network, which injects filtered knowledge to enhance the distillation of valuable user-item collaborative signals. Ultimately, we generate a pruned knowledge graph with lightweight, stable, and robust properties to facilitate the following-up recommendation task. Extensive experiments are conducted on three publicly available datasets to prove the effectiveness and generalization ability of KGTrimmer.
Sociotechnical Implications of Generative Artificial Intelligence for Information Access
Venue: arXiv cs.IR 2024
Year: 2024

Robust access to trustworthy information is a critical need for society with implications for knowledge production, public health education, and promoting informed citizenry in democratic societies. Generative AI technologies may enable new ways to access information and improve effectiveness of existing information retrieval systems but we are only starting to understand and grapple with their long-term social implications. In this chapter, we present an overview of some of the systemic consequences and risks of employing generative AI in the context of information access. We also provide recommendations for evaluation and mitigation, and discuss challenges for future research.
FADet: A Multi-sensor 3D Object Detection Network based on Local Featured Attention
Venue: arXiv cs.CV 2024
Year: 2024

Camera, LiDAR and radar are common perception sensors for autonomous driving tasks. Robust prediction of 3D object detection is optimally based on the fusion of these sensors. To exploit their abilities wisely remains a challenge because each of these sensors has its own characteristics. In this paper, we propose FADet, a multi-sensor 3D detection network, which specifically studies the characteristics of different sensors based on our local featured attention modules. For camera images, we propose dual-attention-based sub-module. For LiDAR point clouds, triple-attention-based sub-module is utilized while mixed-attention-based sub-module is applied for features of radar points. With local featured attention sub-modules, our FADet has effective detection results in long-tail and complex scenes from camera, LiDAR and radar input. On NuScenes validation dataset, FADet achieves state-of-the-art performance on LiDAR-camera object detection tasks with 71.8% NDS and 69.0% mAP, at the same time, on radar-camera object detection tasks with 51.7% NDS and 40.3% mAP. Code will be released at https://github.com/ZionGo6/FADet.
Approximation and Gradient Descent Training with Neural Networks
Venue: arXiv cs.LG 2024
Year: 2024

It is well understood that neural networks with carefully hand-picked weights provide powerful function approximation and that they can be successfully trained in over-parametrized regimes. Since over-parametrization ensures zero training error, these two theories are not immediately compatible. Recent work uses the smoothness that is required for approximation results to extend a neural tangent kernel (NTK) optimization argument to an under-parametrized regime and show direct approximation bounds for networks trained by gradient flow. Since gradient flow is only an idealization of a practical method, this paper establishes analogous results for networks trained by gradient descent.
QComp: A QSAR-Based Data Completion Framework for Drug Discovery
Venue: arXiv cs.LG 2024
Year: 2024

In drug discovery, in vitro and in vivo experiments reveal biochemical activities related to the efficacy and toxicity of compounds. The experimental data accumulate into massive, ever-evolving, and sparse datasets. Quantitative Structure-Activity Relationship (QSAR) models, which predict biochemical activities using only the structural information of compounds, face challenges in integrating the evolving experimental data as studies progress. We develop QSAR-Complete (QComp), a data completion framework to address this issue. Based on pre-existing QSAR models, QComp utilizes the correlation inherent in experimental data to enhance prediction accuracy across various tasks. Moreover, QComp emerges as a promising tool for guiding the optimal sequence of experiments by quantifying the reduction in statistical uncertainty for specific endpoints, thereby aiding in rational decision-making throughout the drug discovery process.
Optimization of Worker Scheduling at Logistics Depots Using Genetic Algorithms and Simulated Annealing
Venue: arXiv cs.NE 2024
Year: 2024

This paper addresses the optimization of scheduling for workers at a logistics depot using a combination of genetic algorithm and simulated annealing algorithm. The efficient scheduling of permanent and temporary workers is crucial for optimizing the efficiency of the logistics depot while minimizing labor usage. The study begins by establishing a 0-1 integer linear programming model, with decision variables determining the scheduling of permanent and temporary workers for each time slot on a given day. The objective function aims to minimize person-days, while constraints ensure fulfillment of hourly labor requirements, limit workers to one time slot per day, cap consecutive working days for permanent workers, and maintain non-negativity and integer constraints. The model is then solved using genetic algorithms and simulated annealing. Results indicate that, for this problem, genetic algorithms outperform simulated annealing in terms of solution quality. The optimal solution reveals a minimum of 29857 person-days.
Self-Supervised Learning of Visual Servoing for Low-Rigidity Robots Considering Temporal Body Changes
Venue: arXiv cs.RO 2024
Year: 2024

In this study, we investigate object grasping by visual servoing in a low-rigidity robot. It is difficult for a low-rigidity robot to handle its own body as intended compared to a rigid robot, and calibration between vision and body takes some time. In addition, the robot must constantly adapt to changes in its body, such as the change in camera position and change in joints due to aging. Therefore, we develop a method for a low-rigidity robot to autonomously learn visual servoing of its body. We also develop a mechanism that can adaptively change its visual servoing according to temporal body changes. We apply our method to a low-rigidity 6-axis arm, MyCobot, and confirm its effectiveness by conducting object grasping experiments based on visual servoing.
Systematic Review on Healthcare Systems Engineering utilizing ChatGPT
Venue: arXiv cs.ET 2024
Year: 2024

This paper presents an analytical framework for conducting academic reviews in the field of Healthcare Systems Engineering, employing ChatGPT, a state-of-the-art tool among recent language models. We utilized 9,809 abstract paragraphs from conference presentations to systematically review the field. The framework comprises distinct analytical processes, each employing tailored prompts and the systematic use of the ChatGPT API. Through this framework, we organized the target field into 11 topic categories and conducted a comprehensive analysis covering quantitative yearly trends and detailed sub-categories. This effort explores the potential for leveraging ChatGPT to alleviate the burden of academic reviews. Furthermore, it provides valuable insights into the dynamic landscape of Healthcare Systems Engineering research.
EPPS: Advanced Polyp Segmentation via Edge Information Injection and Selective Feature Decoupling
Venue: arXiv cs.CV 2024
Year: 2024

Accurate segmentation of polyps in colonoscopy images is essential for early-stage diagnosis and management of colorectal cancer. Despite advancements in deep learning for polyp segmentation, enduring limitations persist. The edges of polyps are typically ambiguous, making them difficult to discern from the background, and the model performance is often compromised by the influence of irrelevant or unimportant features. To alleviate these challenges, we propose a novel model named Edge-Prioritized Polyp Segmentation (EPPS). Specifically, we incorporate an Edge Mapping Engine (EME) aimed at accurately extracting the edges of polyps. Subsequently, an Edge Information Injector (EII) is devised to augment the mask prediction by injecting the captured edge information into Decoder blocks. Furthermore, we introduce a component called Selective Feature Decoupler (SFD) to suppress the influence of noise and extraneous features on the model. Extensive experiments on 3 widely used polyp segmentation benchmarks demonstrate the superior performance of our method compared with other state-of-the-art approaches.
Towards Graph Contrastive Learning: A Survey and Beyond
Venue: arXiv cs.LG 2024
Year: 2024

In recent years, deep learning on graphs has achieved remarkable success in various domains. However, the reliance on annotated graph data remains a significant bottleneck due to its prohibitive cost and time-intensive nature. To address this challenge, self-supervised learning (SSL) on graphs has gained increasing attention and has made significant progress. SSL enables machine learning models to produce informative representations from unlabeled graph data, reducing the reliance on expensive labeled data. While SSL on graphs has witnessed widespread adoption, one critical component, Graph Contrastive Learning (GCL), has not been thoroughly investigated in the existing literature. Thus, this survey aims to fill this gap by offering a dedicated survey on GCL. We provide a comprehensive overview of the fundamental principles of GCL, including data augmentation strategies, contrastive modes, and contrastive optimization objectives. Furthermore, we explore the extensions of GCL to other aspects of data-efficient graph learning, such as weakly supervised learning, transfer learning, and related scenarios. We also discuss practical applications spanning domains such as drug discovery, genomics analysis, recommender systems, and finally outline the challenges and potential future directions in this field.
Post-Quantum Security: Origin, Fundamentals, and Adoption
Venue: arXiv cs.CR 2024
Year: 2024

Nowadays, predominant asymmetric cryptographic schemes are considered to be secure because discrete logarithms are believed to be hard to be computed. The algorithm of Shor can effectively compute discrete logarithms, i.e. it can brake such asymmetric schemes. But the algorithm of Shor is a quantum algorithm and at the time this algorithm has been invented, quantum computers that may successfully execute this algorithm seemed to be far out in the future. The latter has changed: quantum computers that are powerful enough are likely to be available in a couple of years. In this article, we first describe the relation between discrete logarithms and two well-known asymmetric security schemes, RSA and Elliptic Curve Cryptography. Next, we present the foundations of lattice-based cryptography which is the bases of schemes that are considered to be safe against attacks by quantum algorithms (as well as by classical algorithms). Then we describe two such quantum-safe algorithms (Kyber and Dilithium) in more detail. Finally, we give a very brief and selective overview of a few actions currently taken by governments and industry as well as standardization in this area. The article especially strives towards being self-contained: the required mathematical foundations to understand post-quantum cryptography are provided and examples are given.